# IJCAI 2026 review form > Simulate IJCAI-ECAI 2026-style reviews: contribution summary, strengths, opportunities to improve, pressing rebuttal questions, the nine-level overall assessment with justification, reproducibility, expertise and confidence. - Venue: International Joint Conference on Artificial Intelligence - Form status: modeled, not official. IJCAI-ECAI 2026 did not publish its review form. Fields and labels follow the official IJCAI-ECAI 2022 form (the most recent public one); the 2026 pages confirm the criteria, the ethics flag and the Convincing / Credible / Irreproducible reproducibility levels. Numbering of the 9-level overall scale is ours. - Official source: https://ijcai-22.org/wp-content/uploads/2022/01/Review-form-IJCAI-ECAI-2022-2.pdf - Overall score: Overall Assessment, 1–9 - Rough acceptance bar (our simulation): mean ≥ 5 - Web page: https://reviewpaper.ai/venues/ijcai ## How reviewers read the paper IJCAI-ECAI 2026 reviews in two phases: two Phase-1 reviews decide summary rejection without author response, then Phase 2 adds reviews, a one-page author response (no new results) and an AC meta-review cross-checked by a senior AC. Criteria: novelty and originality, correctness, clarity, significance of results, potential impact and presentation quality. Questions for authors must be pressing and answerable without new experiments; missing code, data or full proofs should not be the main reason to reject. - Keep the summary free of evaluation. - Ask only pressing rebuttal questions that do not require new results. - Do not reject mainly for missing code, data or full proofs; rate reproducibility instead. - Professional and unbiased; reviewer identities stay confidential. **Where the bar sits.** IJCAI-ECAI 2026 accepted about 13.5% of main-track papers (secondary source; 2025: 19.3%). A summary-reject phase removes papers whose two Phase-1 reviews are not positive enough, and a rebuttal rarely rescues reject-leaning reviews. On the 1–9 scale a mean of ≥ 5 (Borderline Accept) is needed to be competitive; most accepts average 6+. ## Reviewer form, field by field ### 1. Summary Of Main Contributions Type: free text. Briefly summarize the main contributions in your own words. Do not include your evaluation here. ### 2. Main Strengths Type: list of points. Focus on novelty, soundness, significance and expected impact, relevance to AI, clarity of exposition, and reproducibility (credibility). ### 3. Opportunities To Improve The Paper Type: list of points. Weaknesses and how the paper could be improved, most important first. ### 4. Pressing Questions For The Authors In The Rebuttal Type: list of points. Only questions whose answers could directly change your evaluation and that do not require new results (the one-page rebuttal allows no new experiments). ### 5. Overall Assessment Type: score, 1–9. Overall assessment. Borderline levels sparingly. - **9**: Award Quality: Truly groundbreaking work. This paper is likely to change our field. It should be considered for a best paper award. (Top 5% of accepted IJCAI papers.) - **8**: Strong Accept. Inspiring. An excellent paper. This paper changed my thinking on this topic. I will fight for acceptance. (Top 15% of accepted IJCAI papers). - **7**: Clear Accept. Interesting. A very good submission. I learned a lot from this paper. I vote and argue for acceptance. (Top 50% of accepted IJCAI papers.) - **6**: Weak Accept. Useful. A good paper. The results and insights will benefit the field. I believe it should be accepted. - **5**: Borderline Accept. Marginally above the acceptance threshold. Technically correct, but not particularly exciting or inspiring. Could be accepted more or less in its current form. Not a big loss if it is not included in the program. Please use sparingly. - **4**: Borderline Reject. Marginally below the acceptance threshold. The paper has merits but there are key weaknesses. It would benefit from another revision. Can be rejected. But having it in the program would not be that bad. Please use sparingly. - **3**: Weak Reject. Not good enough. Limited contribution. Needs more work and/or revisions. I believe it should be rejected. - **2**: Clear Reject. Unclear contribution. Needs to be reworked and/or major revision. I vote and will argue for rejection. - **1**: Strong Reject. Contribution is flawed. I will strongly argue for rejection. ### 6. Justification Of Score Type: free text. Justify your score in a few lines. ### 7. Reproducibility Type: single choice. Convincing: results can be reproduced, possibly with some effort; key resources available (or good reasons given) and key details well described. Credible: resources unavailable, but details suffice for an expert given access. Irreproducible: key details incomplete or key resources not furnished. Do not reject mainly for missing code, data or full proofs. - Convincing - Credible - Irreproducible ### 8. Ethical Concerns Type: free text. "No", or "Yes" with a summary of the ethical issues and how the paper's ethics statement handles them (a Yes triggers senior-PC screening). ### 9. Expertise Type: single choice. How well the paper aligns with your expertise. - Expert - Knowledgeable - Somewhat knowledgeable - Not knowledgeable ### 10. Confidence Type: score, 1–4. How confident you are in your evaluation. - **4**: Very confident. I have checked all points of the paper carefully. I am certain I did not miss any important aspects. - **3**: Confident. I tried to check the key points in the paper. It is unlikely, though conceivable, that I missed some important aspects. - **2**: Somewhat confident. I was unable to carefully check some of the details (proof, novelty, experimental design …) but I am able to defend my evaluation of the paper. It is quite likely that I missed some important aspects. - **1**: Not confident. My evaluation is an educated guess. ## Area chair meta-review IJCAI ACs write the meta-review, a senior AC cross-checks it, and SPC/ACs rate review quality (Exceeding / Met / Failed to meet expectations). Recommendation options: Accept · Reject ## IJCAI reproducibility checklist 1. Theory: assumptions and claims stated formally; theoretical tools cited; proof sketches; full proofs 2. Datasets: novel data in an appendix or public; existing data cited and public; private data described 3. Experiments: code in an appendix or public (or reasons given); hyperparameter search ranges and selection criterion; final hyperparameters; compute infrastructure 4. New algorithms: outline or pseudocode ## LLM policy IJCAI-ECAI 2026 lets reviewers use generative AI only to improve the readability of their own review text, after removing anything identifying or confidential; submissions or their technical content must never be uploaded to tools without confidentiality guarantees. Official policy: https://2026.ijcai.org/principles-of-peer-review-and-recognition/ --- Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=ijcai (3 free reviews). Sample report: https://reviewpaper.ai/sample. reviewpaper.ai is for authors checking their own unpublished drafts. If you are an assigned reviewer, follow your venue's LLM policy: most venues forbid putting a submission you are reviewing into any external AI tool. Not affiliated with any conference.